title: Integrated bioinformatics analysis for cancer target identification.
abstract: The exponential growth of high-throughput Omics data has provided an unprecedented opportunity for new target identification to fuel the dried-up drug discovery pipeline. However, the bioinformatics analysis of large amount and heterogeneous Omics data has posed a great deal of technical challenges for experimentalists who lack statistical skills. Moreover, due to the complexity of human diseases, it is essential to analyze the Omics data in the context of molecular networks to detect meaningful biological targets and understand disease processes. Here, we describe an integrated bioinformatics analysis strategy and provide a running example to identify suitable targets for our in-house Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) technology. In addition, we go through a few key concepts in the process, including corrected false discovery rate (FDR), Gene Ontology (GO), pathway analysis, and tissue specificity. We also describe popular programs and databases which allow the convenient annotation and network analysis of Omics data. We provide a practical guideline for researchers to quickly follow the protocol described and identify those targets that are pertinent to their work.
authors.affiliation: Department of Radiology, Harvard Medical School, Harvard University, Boston, MA, USA.
authors.forename: Yongliang
authors.initials: Y
authors.lastname: Yang
authors.forename: S James
authors.initials: SJ
authors.lastname: Adelstein
authors.forename: Amin I
authors.initials: AI
authors.lastname: Kassis
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D006801:Humans; D008093:Literature, Modern; D057054:Molecular Imaging; D009369:Neoplasms / Q000201:enzymology / Q000378:metabolism* / Q000628:therapy; D020411:Oligonucleotide Array Sequence Analysis